| #!/bin/bash | |
| ## This gist contains instructions about cuda v11.2 and cudnn8.1 installation in Ubuntu 20.04 for Pytorch 1.8 & Tensorflow 2.7.0 | |
| ### steps #### | |
| # verify the system has a cuda-capable gpu | |
| # download and install the nvidia cuda toolkit and cudnn | |
| # setup environmental variables | |
| # verify the installation | |
| ### |
Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:
TensorFlow is now distributed under an Apache v2 open source license on GitHub.
On Ubuntu 16.04LTS+:
Step 1. Install NVIDIA CUDA:
To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:
| #http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/ | |
| #https://www.tensorflow.org/versions/r0.7/api_docs/python/constant_op.html#random_uniform | |
| import tensorflow as tf | |
| import numpy as np | |
| import input_data | |
| import Image | |
| from util import tile_raster_images | |
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
| // adapted from intersectCube in https://github.com/evanw/webgl-path-tracing/blob/master/webgl-path-tracing.js | |
| // compute the near and far intersections of the cube (stored in the x and y components) using the slab method | |
| // no intersection means vec.x > vec.y (really tNear > tFar) | |
| vec2 intersectAABB(vec3 rayOrigin, vec3 rayDir, vec3 boxMin, vec3 boxMax) { | |
| vec3 tMin = (boxMin - rayOrigin) / rayDir; | |
| vec3 tMax = (boxMax - rayOrigin) / rayDir; | |
| vec3 t1 = min(tMin, tMax); | |
| vec3 t2 = max(tMin, tMax); | |
| float tNear = max(max(t1.x, t1.y), t1.z); |
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
| *.so | |
| *.o | |
| *.html | |
| .*.un~ | |
| .*.swp |
Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000
| """ | |
| Web Mercator Scale and Resolution Calculations | |
| Python implementation of the formulas at http://msdn.microsoft.com/en-us/library/bb259689.aspx | |
| """ | |
| import math | |
| def meters_per_pixel(zoom, lat): | |
| """ | |
| ground resolution = cos(latitude * pi/180) * earth circumference / map width | |
| """ |